The need for analyzing the different crime dataset has significantly increased over the past few decades. It is necessary to detect a wide variety of crimes and the corresponding place of occurrences accurately. Government bodies throughout the world maintained Open Data initiatives, which is an extensive collection of a heterogeneous dataset. This enables government agencies to keep the law and order of society. The prime objective of this paper is to analyze the Chicago crime dataset to extract important crime information over the years. The proposed analysis is performed to bring out the crime information depending on some predetermined criteria, e.g., total crimes of different types, a subtype of various crimes, crime on different places, analysis of hourly crime cases, and analysis of criminal cases on different location. The proposed study is performed in a fully distributed Hadoop cluster. Moreover, we have extracted minuscule vital statistics on weekday illegal activities. Though Chicago crime data is not too big to be analyzed on clusters but, for the learning purpose, to know about the cluster setup and learn to work in a distributed environment, we did this analysis.
(Data source - https://data.cityofchicago.org/Public-Safety/Crimes-2019/w98m-zvie)
The objective of the project is to provide the users with different methods to analyze the crime rate of a Chicago city and give different kinds of analysis based on some parameters like Time, Location, and Crime types, which is useful to predict the crime rate in future based on some location and time. So, they can reach before the incident on that place and prevent crime incidents.
Ubuntu - 18.04.3
Java - jdk-8u201-linux-x64
Hadoop - 2.6.5
Pig - 0.16.0
| Master node | Slave Node1 | Slave Node2 | Slave Node3 | |
|---|---|---|---|---|
| Machine Name | M1 | M2 | M3 | M4 |
| HDFS Components | Name Node Data Node |
Secondary Name Node Data Node |
Data Node | Data Node |
| MapReduce Components | Job Tracker Task Tracker |
Task Tracker | Task Tracker | Task Tracker |
| Network Address | 192.168.56.11 | 192.168.56.10 | 192.168.56.12 | 192.168.56.13 |
(https://github.com/JayGolakiya/Chiago-Crime-Analysis/tree/master/Timeseries)
- Total number of crime happened in each past year
- Total number of crime per month
- Total Number of crime per day
- Total Number of crime per hour
- Number of crime in each year of particular crime type
(https://github.com/JayGolakiya/Chiago-Crime-Analysis/tree/master/Statistical)
- Description of location where the crime was happened
- Different Crime Types and criminal arrested or not
(https://github.com/JayGolakiya/Chiago-Crime-Analysis/tree/master/Geospatial)
- District location and number of arrest in each district
Till now, we had calculated a different kind of analysis. This analysis is in numeric format. It is challenging to visualize text data and make a decision at a glance. For easy visualization and easy understanding by any non-technical and any person, we plotted analyzed data on different types of graphs. These graphs, like the Bar chart, Pie chart, and Line chart. Based on data, different kinds of visualizations are prepared. For the implementation of that functionalities, we had used the Django framework and chart.js graph library.
Django version: 2.0.7
ChartJS CDNs :
https://cdn.jsdelivr.net/npm/[email protected]/dist/Chart.min.js
https://cdnjs.cloudflare.com/ajax/libs/Chart.js/2.1.4/Chart.min.js